Use of recommendations in search engines is a trend of search engine development. A need for recommendations is particularly important in a wireless search scenario, because the cost for a user to obtain information can be higher on a relatively small screen in a wireless search scenario. The user expects that a machine could better understand the demand of the user and provide recommendations of similar query information while satisfying a current search query. Therefore, it is particularly important with regard to the involvement of a recommendation in a search.
In existing search engines, there are roughly two types of scenario where recommendations are used. One type is that some personalized recommendations are provided to a user on a homepage according to analysis of historical behaviors of the user, thereby achieving an effect of obtaining without searching. For example, a user has paid more attention to a certain vehicle recently, searched many queries related to this vehicle, and browsed many websites related to this vehicle. By analyzing the user's behaviors, it can be concluded that the user is interested in the vehicle, and the latest news and videos on the same type can be recommended. The other type is that recommendation contents are provided to a user while the user is searching an exemplary query. For example, when a user is searching Magotan used cars, relevant queries are provided, such as quotes for Magotan used cars, minor issues regarding repair of Magotan used cars, and the like.
The above-mentioned two types of recommendations both involve a key technology, i.e., establishment of association of relevant words, which can also be interpreted as relevant word clustering. For example, auto repair, used car, and Magotan used cars can be clustered into one class. Alternatively, it can be understood that auto repair has a close relationship with used cars, while auto repair does not have a more distant relationship with other non-auto related words.
The inventors of the present invention have realized that existing keyword relevance recommendation technologies can have some issues. For example, it is impossible to provide keyword recommendations that are more pertinent for more refined texts in an industry. As an example, keyword recommendations of sports-related topics can be accurately obtained, but it would be more difficult to obtain keyword recommendations for race cars, a secondary classification of sports.